Open Malaria Warning

A study by Loha and colleges in Plos One looked at the effect of IRS and ITNs in a village in Southern Ethiopia:

The level of ITN utilization increased after mass distribution of ITNs; however it did not lower the risk for malaria clustering. Therefore, it is possible that free mass distribution of ITNs is not an effective tool with which to combat malaria without follow-up to ensure the optimal utilization of the ITNs.

Of course, there are several studies showing the effect of bed net usage on community malaria incidence, but there is a lack of studies showing the effectiveness of ITNs dependent on the involved vector. In the study are Anopheles arabiensis is the dominant vector, a mosquito which feed indoor as well as outdoor, and possibly before people go to bed.

This week Dori et al. published an interesting study about perceptions of malaria in the Konso community in Ethiopia:

Using a structured questionnaire, focus group discussions and in-depth interviews, this study aimed to gain deeper insight on how the Konso community in Ethiopia perceives malaria and manages the disease. Although knowledge about malaria was above average it surfaced that the use of herbal home remedies is widespread. It is argued that this practice warrants further investigation to validate the efficacy and safety of plant preparations that are employed.

The MW-journal was new to me. It is as open access journal which does not charge the authors (or the readers) for publishing articles. It will be interesting to see if the community embraces this journal, and if it is a threat to Malaria Journal.

In order to work with netcdf-data and at the same time use sp-methods in R it is convenient to have a spatial format that supports time. A new R format, Spatial3dArray might be one way to deal with these kind of data. To use the new class you have to load sp.

setClass( "Spatial3dArray",
          representation("Spatial", data = "array", coords = "list",
                          time = "character", btime = "character"),
          prototype= list(data = array(NA, c(1,1,1,1)),
                          bbox=matrix(NA),
                          proj4string = CRS(as.character(NA)),
                          coords = list(1,1),
                          time = "posix",
                          btime = "posix"))

An example on a new Spatial3dArray can be:

x <- matrix(seq(-10, 10, length = 150), 150, 150,
            byrow = FALSE)
y <- matrix(seq(-10, 10, length = 150), 150, 150,
            byrow = TRUE)

tm <- 1:10
tm.c <- as.character(seq(as.POSIXct("2002-01-01 06:00:00",
                                    "2002-01-01 15:00:00"),
                          by=1,
                          length.out=10))

z <- array(NA, c(dim(x)[1], dim(x)[2], length(tm.c), 1))

for (i in 1:10) {
a <- c(seq(1,6), rev(seq(2,5)))[i]
b <- c(seq(1, 2, length.out = 5), rev(seq(1, 2, length.out = 5)))
z[,,i,] <- a/10 * ( sin(sqrt((x*b[i])^2+(y*b[i])^2)))
}

sin3dA <- new("Spatial3dArray",
      data = z,
      coords = list(x, y),
      bbox = matrix(c(min(x), min(y), max(x), max(y), 2, 2), 2, 2,
      dimnames = list(NULL, c("min","max"))),
      time = tm.c,
      btime = c(min(tm.c), max(tm.c)))

dimnames(slot(sin3dA, "data")) = list(NULL,
                                      NULL,
                                      slot(sin3dA, "time"),
                                      c("a"))
names(slot(sin3dA, "coords")) <- c("x", "y")

Now usual spatial methods can be used to retrieve bbox and proj4string. Since Spatial3dArray stores coordinates in a different way then the usual sp-objects we have to define a new function to get the coordinates (a list with two matrices):

coordinates.3dArray <- function (obj, type = "list") {
  if (is(obj, "Spatial3dArray")) {
  lat <- slot(obj, "coords")[[1]]
  long <- slot(obj, "coords")[[2]]
  if (type == "list") {
    return(list(lat=lat, long=long))
    } else if (type == "sp") {
        res <- as.matrix(cbind(c(long), c(lat)))
        dimnames(res) <- list(NULL, c("x1", "x2"))
        return(res)
      }
  }
}
setMethod("coordinates", signature("Spatial3dArray"), coordinates.3dArray)

So plotting with lattice wireframe and Graphics Magic would give something like:

I will come back with more methods related to the Spatial3dArray.

It seems like we are getting the grip on both dynamical downscaling and running things to the supercomputer. At the moment we are running the model for one week (2005 data). Tomorrow morning we expect the computations to finish. Then, once again, it is time to break everything and feed the model with even more data. I guess that is how it works; once you have something that is working, the fun is gone, and you have to make improvements which likely will ruin the setup.